Analytics is about automating the looking for patterns and relationships, predicting outcomes and automating some decisions. And this can take much of the pain out of trade promotion effectiveness evaluation (whether using analysis or analytics).
Trade promotion analysis. A necessary evil, but a phrase to make some people sigh, and others shudder. A potential minefield of data of varying quantity and quality to pick your way through, often from multiple sources.
But trade promotion analysis, and analytics (there’s a difference) don’t have to be anxiety-inducing.
Sure, there are a number of challenges that can lead to trade promotion analysis paralysis, or even avoidance. Among other things, these include:
- Not knowing what the fundamental questions and problems are, or how to analyze them
- Having an overwhelming amount of data, or conversely, a paucity of it
- A lack of visibility of or confidence in the data used
- Not knowing the key data points and metrics required to make trade promotion decisions, or how to present it so it makes sense and is easily understood
- A lack of understanding where the data was sourced, the calculations applied to it, or how old it is
- Data not in a proper or usable form, including spreadsheets
- Needing to source data from multiple sources in order to analyze
- High dependence on IT to create dashboards and refresh reports
- Lack of organizational capability to conduct pre and post event analysis and manage effectiveness of promotional programs.
All of this means there may well be trade overspends and potential revenue left on the proverbial table, as well as difficulty getting retailers to accept proposed promotions programs. Gaining visibility and automating analysis is key, and this can be difficult if promotions are managed through a series of Excel spreadsheets.
Know where to start
Knowing where to start and what the key promotional metrics are can help avoid boiling the ocean. Make it easier on yourself by:
- Setting up a dashboard of key metrics needed to evaluate each promotion.
- Using of Out Of the Box ‘canned’ reports by software providers and other suppliers
- Reporting by exception – just the problem areas rather than ‘everything’
- Configuring scheduled reports and dashboards to show only relevant information to the problem you are trying to solve or the KPI you are trying to measure
- Devising simple to configure ad-hoc reporting
- Classifying data into groups with units and toggles to allow for simple and quick analysis.
Shifting gear from manual analysis to automatic analytics
It’s one thing to know where to start and what to analyze. It’s another to actually do it.
‘Analysis’ is typically manual – looking over a lot of different sources to try to extract a story. ‘Analytics’, on the other hand – whilst being an overused word and an increasingly broad term – is really about automation.
Wikipedia defines analytics as “the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns toward effective decision-making.”
Analytics is about automating the looking for patterns and relationships, predicting outcomes and automating some decisions. And this can take much of the pain out of trade promotion effectiveness evaluation (whether using analysis or analytics).
Automated analytics can be used to:
- Set up promotion KPI dashboards and benchmarks answering the TPO fundamentals such as gross sales performance, profit, ROI, with flags for underperformance. This enables real-time understanding of how a trade strategy is performing across channels and customers, for adjustment where necessary
- Monitor KPIs such as pending and rejected claims, spending by component, and volume fulfillment.
- Accurately predict future promotions with dynamic scenario planning and embedded TPO
- Ensure alignment to corporate objectives with promotional guardrail functionality broadcast through automated alerts
- Standardize reports such as distributor performance, estimated vs. actuals by promotion, and market support spend against promotion budget
- Enable P&L at customer & product levels
- Pre-aggregate and roll up data
- Report in real time; trends year on year and month on month
- Provide data certainty through identifying where data was sourced, what calculations were applied, how old the information is
- Provide a ‘single source of truth’ available across the organization to multiple users, that can be overlaid with other data sources such as store back measures, competitive data, retailer loyalty data and POS/scan.
Good quality data is obviously fundamental for sales, promotion, demand and supply forecasting, planning and implementation. With the right data, you can enhance business performance, profitability, agility, visibility, and alignment. But good data in and of itself isn’t enough. By investing in a trade promotion system that provides automated analytics capability, not merely data collection for manual analysis, you can focus on decision making rather than doing.
Learn more about using data analytics for Trade Promotion Optimization, or schedule a demo.